arXiv Machine Learning
HMB-GAN: Hybrid Multi-B\'ezier GAN for Vector Shape Synthesis
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arXiv AI
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CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation
arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.
Hugging Face Trending Papers
Aug 12
CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation
Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches face two key limitations: pixel locations are typically encoded by computational-basis indices or address qubits, causing quantum resources to grow with image resolution; meanwhile, jointly decoding many pixels from normalized quantum states introduces probability competition among pixels and limits precise pixel-wise control.
arXiv AI
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IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry
arXiv:2607. 11497v1 Announce Type: cross Abstract: Existing generative models learn data distributions in flat Euclidean space.
arXiv Computer Vision
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ToW3D: Consistency-aware Interactive Point-based Mesh Editing on GANs
The paper introduces ToW3D, a method for precise and consistent control over 3D generative adversarial networks (GANs) using a Tug-of-War approach between shape deformation and appearance consistency. It addresses the challenge that 3D generators often lack generalization, leading to drastic global appearance changes when editing local mesh areas. ToW3D employs a two-step optimization—drag locally and shove globally—along with a structure adaptation module and a semantic preservation module, achieving superior appearance consistency and fidelity compared to prior methods, especially under large deformations.
arXiv Computer Vision
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Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation
Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation explores a simpler pixel‑level, end‑to‑end approach to quantum generative adversarial networks (QGANs) that avoids patch‑based decomposition. The authors introduce the Quantum Fidelity Landscape (QFL) as an invariant pairwise‑fidelity structure preserved under shared unitary transformations, and use it to calibrate the quantum prior before adversarial training. Their BasicQGAN framework aligns the prior‑induced QFL with the data‑induced QFL, achieving stable, effective image generation on small‑scale grayscale datasets while requiring fewer qubits and trainable parameters than patch‑based quantum generators.
arXiv Machine Learning
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Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation
arXiv:2603. 00233v2 Announce Type: replace-cross Abstract: Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning.